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Future ERP Systems
(2021)
This paper presents a research agenda on the current generation of ERP systems which was developed based on a literature review on current problems of ERP systems. The problems are presented following the ERP life cycle. In the next step, the identified problems are mapped on a reference architecture model of ERP systems that is an extension of the three-tier architecture model that is widely used in practice. The research agenda is structured according to the reference architecture model and addresses the problems identified regarding data, infrastructure, adaptation, processes, and user interface layer.
While Information Systems Research exists at the individual and workgroup levels, research on IS at the enterprise level is less common. The potential synergies between the study of enterprise systems (ES) and related fields have been underexplored and often treated as separate entities. The ongoing challenge is to seamlessly integrate technological advances and align business processes across organizations. While systems integration within an organization is common, changes occur when industry and ecosystem perspectives come into play. The four selected papers address different facets of the future role of enterprise ecosystems, including implementation challenges, ecosystem boundaries, and B2B platform specifics.
In this thesis sentence processing was investigated using a psychophysiological measure known as pupillometry as well as Event-Related Potentials (ERP). The scope of the the- sis was broad, investigating the processing of several different movement constructions with native speakers of English and second language learners of English, as well as word order and case marking in German speaking adults and children. Pupillometry and ERP allowed us to test competing linguistic theories and use novel methodologies to investigate the processing of word order. In doing so we also aimed to establish pupillometry as an effective way to investigate the processing of word order thus broadening the methodological spectrum.
Moving Beyond ERP Components
(2018)
Relationships between neuroimaging measures and behavior provide important clues about brain function and cognition in healthy and clinical populations. While electroencephalography (EEG) provides a portable, low cost measure of brain dynamics, it has been somewhat underrepresented in the emerging field of model-based inference. We seek to address this gap in this article by highlighting the utility of linking EEG and behavior, with an emphasis on approaches for EEG analysis that move beyond focusing on peaks or "components" derived from averaging EEG responses across trials and subjects (generating the event-related potential, ERP). First, we review methods for deriving features from EEG in order to enhance the signal within single-trials. These methods include filtering based on user-defined features (i.e., frequency decomposition, time-frequency decomposition), filtering based on data-driven properties (i.e., blind source separation, BSS), and generating more abstract representations of data (e.g., using deep learning). We then review cognitive models which extract latent variables from experimental tasks, including the drift diffusion model (DDM) and reinforcement learning (RL) approaches. Next, we discuss ways to access associations among these measures, including statistical models, data-driven joint models and cognitive joint modeling using hierarchical Bayesian models (HBMs). We think that these methodological tools are likely to contribute to theoretical advancements, and will help inform our understandings of brain dynamics that contribute to moment-to-moment cognitive function.
Moving beyond ERP components
(2018)
Relationships between neuroimaging measures and behavior provide important clues about brain function and cognition in healthy and clinical populations. While electroencephalography (EEG) provides a portable, low cost measure of brain dynamics, it has been somewhat underrepresented in the emerging field of model-based inference. We seek to address this gap in this article by highlighting the utility of linking EEG and behavior, with an emphasis on approaches for EEG analysis that move beyond focusing on peaks or "components" derived from averaging EEG responses across trials and subjects (generating the event-related potential, ERP). First, we review methods for deriving features from EEG in order to enhance the signal within single-trials. These methods include filtering based on user-defined features (i.e., frequency decomposition, time-frequency decomposition), filtering based on data-driven properties (i.e., blind source separation, BSS), and generating more abstract representations of data (e.g., using deep learning). We then review cognitive models which extract latent variables from experimental tasks, including the drift diffusion model (DDM) and reinforcement learning (RL) approaches. Next, we discuss ways to access associations among these measures, including statistical models, data-driven joint models and cognitive joint modeling using hierarchical Bayesian models (HBMs). We think that these methodological tools are likely to contribute to theoretical advancements, and will help inform our understandings of brain dynamics that contribute to moment-to-moment cognitive function.
The current study examines the neural correlates of 8-to-12-year-old children and adults producing inflected word forms, specifically regular vs. irregular past-tense forms in English, using a silent production paradigm. ERPs were time-locked to a visual cue for silent production of either a regular or irregular past-tense form or a 3rd person singular present tense form of a given verb (e.g., walked/sang vs. walks/sings). Subsequently, another visual stimulus cued participants for an overt vocalization of their response. ERP results for the adult group revealed a negativity 300-450 ms after the silent-production cue for regular compared to irregular past-tense forms. There was no difference in the present form condition. Children's brain potentials revealed developmental changes, with the older children demonstrating more adult-like ERP responses than the younger ones. We interpret the observed ERP responses as reflecting combinatorial processing involved in regular (but not irregular) past-tense formation.
Background: Event-related potentials (ERPs) are increasingly used in cognitive science. With their high temporal resolution, they offer a unique window into cognitive processes and their time course. In this paper, we focus on ERP experiments whose designs involve selecting participants and stimuli amongst many. Recently, Westfall et al. (2017) highlighted the drastic consequences of not considering stimuli as a random variable in fMRI studies with such designs. Most ERP studies in cognitive psychology suffer from the same drawback. New method: We advocate the use of the Quasi-F or Mixed-effects models instead of the classical ANOVA/by-participant F1 statistic to analyze ERP datasets in which the dependent variable is reduced to one measure per trial (e.g., mean amplitude). We combine Quasi-F statistic and cluster mass tests to analyze datasets with multiple measures per trial. Doing so allows us to treat stimulus as a random variable while correcting for multiple comparisons. Results: Simulations show that the use of Quasi-F statistics with cluster mass tests allows maintaining the family wise error rates close to the nominal alpha level of 0.05. Comparison with existing methods: Simulations reveal that the classical ANOVA/F1 approach has an alarming FWER, demonstrating the superiority of models that treat both participant and stimulus as random variables, like the Quasi-F approach. Conclusions: Our simulations question the validity of studies in which stimulus is not treated as a random variable. Failure to change the current standards feeds the replicability crisis.
Language can strongly influence the emotional state of the recipient. In contrast to the broad body of experimental and neuroscientific research on semantic information and prosodic speech, the emotional impact of grammatical structure has rarely been investigated. One reason for this might be, that measuring effects of syntactic structure involves the use of complex stimuli, for which the emotional impact of grammar is difficult to isolate. In the present experiment we examined the emotional impact of structural parallelisms, that is, repetitions of syntactic features, on the emotion-sensitive "late positive potential" (LPP) with a cross-modal priming paradigm. Primes were auditory presented nonsense sentences which included grammatical-syntactic parallelisms. Visual targets were positive, neutral, and negative faces, to be classified as emotional or non-emotional by the participants. Electrophysiology revealed diminished LPP amplitudes for positive faces following parallel primes. Thus, our findings suggest that grammatical structure creates an emotional context that facilitates processing of positive emotional information.